Applying permutation tests with adjustment for covariates and attrition weights to randomized trials of health-services interventions.
Using a health-services study as an illustrative example of longitudinal randomized field research with the potential for participants to be lost to follow-up, we apply a permutation test where the treatment indicator variable is randomly permuted in the context of regression models with covariates and attrition weighting. The test is applied to a multi-site randomized intervention trial of a quality-improvement program for adolescent depression treatment in primary-care settings, in which regression models were used to assess intervention effects with weights used to adjust for attrition bias. The foundation and motivation for this approach to the analysis are considered with attention to the demands associated with implementing such a strategy. The results from the permutation tests were qualitatively similar to the results obtained from conventional parametric models, and in fact suggested that the significance level from the conventional t-test was understated in this application.
Tang
Lingqi
L
Health Services Research Center, Semel Institute for Neuroscience and Human Behavior, UCLA, 10920 Wilshire Boulevard, Suite 300, Los Angeles, CA 90024, USA. lqtang@ucla.edu
Duan
Naihua
N
Klap
Ruth
R
Asarnow
Joan Rosenbaum
JR
Belin
Thomas R
TR
D000293:Adolescent; D000704:Analysis of Variance; D003863:Depression / Q000628:therapy; D006801:Humans; D010352:Patient Dropouts; D011320:Primary Health Care; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012044:Regression Analysis; D016896:Treatment Outcome